If you were not able to catch the webinar live, don’t worry. In this blog, we’ll highlight the important moments you missed from the event. During the webinar, we heard from Rigvi Chevala and Mohit Bhakuni.
CTO of Evalueserve
CEO & Founder of Contify
Our two speakers discussed the current state of AI and industry expertise within the competitive intelligence field, tactics for building a successful competitive intelligence program, and the future of AI and industry knowledge, and how it impacted competitive intelligence.
Current State of AI and Industry Expertise within Competitive Intelligence
Mohit spoke about key intelligence questions to ask about your market and competitive intelligence program:
- What decisions are to be made?
- What strategic inputs are required for those decisions?
“The right information is buried under the ocean of irrelevant and duplicate information. To make sense of it by searching manually is impossible. To process digital information at internet scale, we need technologies, broadly AI, to not only automate the collection of information but also to filter and structure it so that the analysis becomes easy.”
To hear more of Mohit’s insight into the key intelligence questions, check out the on-demand webinar.
Rigvi continued on the same path as Mohit and explained the AI model with an analogy to a paint mixer.
“In this example, an AI model boils down to the weightage of each color that needs to be added to arrive at the outcome. Not knowing the combination up front is fine as long as there’s enough iterations of trial and error to figure out the weights eventually. This is where AI ends though. In this analogy, industry expertise comes in when someone defines why “lime green” is the intended output. Just like an experienced interior designer who knows that perfect shade of lime green needed for a particular project.”
Tactics of Building a Successful Competitive Intelligence Program
The Ultimate Guide to:
A Successful Competitive Intelligence Program
Our moderator asked, “How are your customers using an AI-only approach and what challenges are they trying to solve? Rigvi responded with insight into the Competitive Intelligence Value Chain.
“We’ve seen a lot of customers trying to use pure-play AI only approaches but have reported lesser success rates as you move along from left to right on the value chain. On the left side, scouring vast amounts of data sources, identifying keywords and using NLP to figure out sentiment is a capability that can be almost AI only.
However, in order to determine adjacencies, understand true context and proper synthesis, identifying false positives, detecting patterns in information using domain knowledge is not really possible using an AI only approach.
Customers who are satisfied with an AI only approach typically have a different need - a superficial research solution that gets them to a thematic level or broad strokes trend sensing.”
Mohit then shared insight into common traps and pitfalls that he’s seen people all into when building a CI program.
“One of the most common traps that we’ve seen is taking a technology first approach and an unhealthy obsession with state-of-the-art technology without clarity on the decisions that CI is supposed to support.
Another common trap is having unrealistic expectations. Sometimes we get customers with expectations that the CI program should tell them things that they don’t already know. Even though in their head the expectation might be correct, but such vague expectations are the recipe for setting up the CI program for failure. These lead to disappointments in the end and, we say that the CI program didn’t perform.”
The Future of AI and Industry Knowledge and How it Will Impact Competitive Intelligence
The last topic of the webinar was the future of AI and industry knowledge and how it will impact competitive intelligence. The first question was “what is AI’s impact on competitive intelligence currently and what are some limitations? Rigvi shared:
“At a high level, the entire workflow of CI involves tens of sub steps that requires quite a bit of manual intervention. While not everything can be replaced or enhanced by AI, even a few steps that can be automated will have a significant impact on where the analysts end up spending time.”
Mohit’s response went on to share some of the limitations that make CI difficult to implement, certain myths surrounding AI, and the strengths and weaknesses of AI and human intelligence. Check out the full webinar to hear his response!
The webinar was ended with both Rigvi and Mohit sharing their final thoughts on how people can effectively build their CI programs by pairing AI with industry experts. Mohit closed out his thoughts first.
“The recipe for success is somewhere in the middle. Success requires the right mix of human instinct with machine intelligence. To combine the two, we need people who can speak the language of both industry and technology. Machines are a tool to outsource our labor. History proves that humans have performed at the top of their capabilities when they partner with machines.”
Rigvi finished his thoughts with this statement.
“I want to emphasize what Mohit mentioned - the recipe for success is somewhere in the middle. The combination of machine learning along with industry domain expertise is what I believe yields the best outcome for an effective CI program. It’s also worth mentioning that the mere combination is not sufficient, it also requires solution providers (such as us) to ensure the program is exposed in a turn-key format with quick onboarding and simplified subscription packages that start producing outcomes at the earliest.”
The Secret to a Successful Market & Competitive Intelligence Program
*Some comments were edited for brevity.